ArticleJournal of pharmacokinetics and pharmacodynamics2012
Serial correlation in optimal design for nonlinear mixed effects models.
Article in Journal of pharmacokinetics and pharmacodynamics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- "De-Shrinking" EBEs: The Solution for Bayesian Therapeutic Drug Monitoring.Clinical pharmacokinetics · 2022Article
- Tutorial for $DESIGN in NONMEM: Clinical trial evaluation and optimization.CPT: pharmacometrics & systems pharmacology · 2021Article
- Reduced and optimized trial designs for drugs described by a target mediated drug disposition model.Journal of pharmacokinetics and pharmacodynamics · 2018Article
- The effect of Fisher information matrix approximation methods in population optimal design calculations.Journal of pharmacokinetics and pharmacodynamics · 2016Article
- Inter occasion variability in individual optimal design.Journal of pharmacokinetics and pharmacodynamics · 2015Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
In population modeling two sources of variability are commonly included; inter individual variability and residual variability. Rich sampling optimal design (more samples than model parameters) using these models will often result in a sampling schedule where some measurements are taken at exactly the same time point, thereby maximizing the signal-to-noise ratio. This behavior is a result of not appropriately taking into account error generation mechanisms and is often clinically unappealing and may be avoided by including intrinsic variability, i.e. serially correlated residual errors. In this paper we extend previous work that investigated optimal designs of population models including serial correlation using stochastic differential equations to optimal design with the more robust, and analytic, AR(1) autocorrelation model. Further, we investigate the importance of correlation strength, design criteria and robust designs. Finally, we explore the optimal design properties when estimating parameters with and without serial correlation. In the investigated examples the designs and estimation performance differs significantly when handling serial correlation.
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Registered trials
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